A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression

Accurate prediction of monthly oil and gas production is essential for oil enterprises to make reasonable production plans, avoid blind investment and realize sustainable development. Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise, and the application conditions are very demanding. With the rapid development of artificial intelligence technology, big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development. Based on the data-driven artificial intelligence algorithm Gradient Boosting Decision Tree (GBDT), this paper predicts the initial single-layer production by considering geological data, fluid PVT data and well data. The results show that the GBDT algorithm prediction model has great accuracy, significantly improving efficiency and strong universal applicability. The GBDT method trained in this paper can predict production, which is helpful for well site optimization, perforation layer optimization and engineering parameter optimization and has guiding significance for oilfield development.

Automated summary

Accurate prediction of monthly oil and gas production is crucial for oil enterprises to plan production, avoid blind investment, and achieve sustainable development. This paper predicts initial single-layer production by utilizing the data-driven artificial intelligence algorithm GBDT, considering geological data, fluid PVT data, and well data. The results demonstrate that the GBDT algorithm has high accuracy, improves efficiency significantly, and has universal applicability. The trained GBDT method in this study can provide helpful predictions for well site optimization, perforation layer optimization, and engineering parameter optimization, thus guiding oilfield development.